In some approaches to sponsored search modeling, a single model is used to globally predict the probability of events (e.g. clicks) based on a user, a query, and characteristics of an advertisement. However, such a single model may not be able to accurately characterize all sources of variability observed in the data. For example, “click-through rates” (CTRs) vary among queries depending upon, for example, the commercial nature of the query. Similarly, some users are a priori more likely to click on ads than other users. In some approaches, a maximum-entropy (“ME”) model is used for click prediction. However, even using such a maximum-entropy model, a single model may not have enough complexity to characterize the data for maximum (or even for improved) predictive results.
Accordingly, there exists a need for improved modeling techniques for estimating probabilities of events in sponsored search display advertising.